Search Results for author:
Found 33 papers, 13 papers with code
CounterCLR: Counterfactual Contrastive Learning with Non-random Missing Data in Recommendation
Recommender systems are designed to learn user preferences from observed feedback and comprise many fundamental tasks, such as rating prediction and post-click conversion rate (pCVR) prediction.
Unified Language-Vision Pretraining in LLM with Dynamic Discrete Visual Tokenization
Specifically, we introduce a well-designed visual tokenizer to translate the non-linguistic image into a sequence of discrete tokens like a foreign language that LLM can read.
From Known to Unknown: Knowledge-guided Transformer for Time-Series Sales Forecasting in Alibaba
Except for introducing future knowledge for prediction, we propose Aliformer based on the bidirectional Transformer, which can utilize the historical information, current factor, and future knowledge to predict future sales.
End-to-End User Behavior Retrieval in Click-Through RatePrediction Model
Recently, researchers have found that the performance of CTR model can be improved greatly by taking user behavior sequence into consideration, especially long-term user behavior sequence.
GRN: Generative Rerank Network for Context-wise Recommendation
Specifically, we first design the evaluator, which applies Bi-LSTM and self-attention mechanism to model the contextual information in the labeled final ranking list and predict the interaction probability of each item more precisely.
Explore User Neighborhood for Real-time E-commerce Recommendation
On the one hand, it utilizes UI relations and user neighborhood to capture both global and local information.
Revisit Recommender System in the Permutation Prospective
Afterwards, for the candidate list set, the PRank stage provides a unified permutation-wise ranking criterion named LR metric, which is calculated by the rating scores of elaborately designed permutation-wise model DPWN.
Towards Long-term Fairness in Recommendation
We focus on the fairness of exposure of items in different groups, while the division of the groups is based on item popularity, which dynamically changes over time in the recommendation process.
Safe Coupled Deep Q-Learning for Recommendation Systems
Firstly, we introduce a novel training scheme with two value functions to maximize the accumulated long-term reward under the safety constraint.
Personalized Adaptive Meta Learning for Cold-start User Preference Prediction
Recently, many existing studies regard the cold-start personalized preference prediction as a few-shot learning problem, where each user is the task and recommended items are the classes, and the gradient-based meta learning method (MAML) is leveraged to address this challenge.
Learning User Representations with Hypercuboids for Recommender Systems
Furthermore, we present two variants of hypercuboids to enhance the capability in capturing the diversities of user interests.
Commonsense knowledge adversarial dataset that challenges ELECTRA
To investigate machine comprehension models' ability in handling the commonsense knowledge, we created a Question and Answer Dataset with common knowledge of Synonyms (QADS).
MTBRN: Multiplex Target-Behavior Relation Enhanced Network for Click-Through Rate Prediction
In this paper, we propose a new framework named Multiplex Target-Behavior Relation enhanced Network (MTBRN) to leverage multiplex relations between user behaviors and target item to enhance CTR prediction.
Understanding Echo Chambers in E-commerce Recommender Systems
Current research on recommender systems mostly focuses on matching users with proper items based on user interests.
Semi-supervised Collaborative Filtering by Text-enhanced Domain Adaptation
This causes two difficulties in designing effective algorithms: first, the majority of users only have a few interactions with the system and there is no enough data for learning; second, there are no negative samples in the implicit feedbacks and it is a common practice to perform negative sampling to generate negative samples.
ATBRG: Adaptive Target-Behavior Relational Graph Network for Effective Recommendation
Specifically, to associate the given target item with user behaviors over KG, we propose the graph connect and graph prune techniques to construct adaptive target-behavior relational graph.
EdgeRec: Recommender System on Edge in Mobile Taobao
Recommender system (RS) has become a crucial module in most web-scale applications.
Privileged Features Distillation at Taobao Recommendations
To guarantee the consistency of off-line training and on-line serving, we usually utilize the same features that are both available.
Query-based Interactive Recommendation by Meta-Path and Adapted Attention-GRU
A novel query-based interactive recommender system is proposed in this paper, where \textbf{personalized questions are accurately generated from millions of automatically constructed questions} in Step 1, and \textbf{the recommendation is ensured to be closely-related to users' feedback} in Step 2.
Exact-K Recommendation via Maximal Clique Optimization
This paper targets to a novel but practical recommendation problem named exact-K recommendation.
Behavior Sequence Transformer for E-commerce Recommendation in Alibaba
Deep learning based methods have been widely used in industrial recommendation systems (RSs).
Ranked #9 on
Recommendation Systems
on MovieLens 1M
Learning Representations of Categorical Feature Combinations via Self-Attention
In most current DNN based models, feature embeddings are simply concatenated for further processing by networks.
Compositional Network Embedding
Node ID is not generalizable and, thus, the existing methods have to pay great effort in cold-start problem.
Personalized Re-ranking for Recommendation
Ranking is a core task in recommender systems, which aims at providing an ordered list of items to users.
BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer
To address this problem, we train the bidirectional model using the Cloze task, predicting the masked items in the sequence by jointly conditioning on their left and right context.
Ranked #2 on
Recommendation Systems
on MovieLens 1M
(HR@10 (full corpus) metric)
Value-aware Recommendation based on Reinforced Profit Maximization in E-commerce Systems
Existing recommendation algorithms mostly focus on optimizing traditional recommendation measures, such as the accuracy of rating prediction in terms of RMSE or the quality of top-$k$ recommendation lists in terms of precision, recall, MAP, etc.
Learning to Collaborate: Multi-Scenario Ranking via Multi-Agent Reinforcement Learning
The first one is lack of collaboration between scenarios meaning that each strategy maximizes its own objective but ignores the goals of other strategies, leading to a sub-optimal overall performance.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Multi-Source Pointer Network for Product Title Summarization
For the second constraint, we restore the key information by copying words from the knowledge encoder with the help of the soft gating mechanism.
Perceive Your Users in Depth: Learning Universal User Representations from Multiple E-commerce Tasks
In this work, we propose to learn universal user representations across multiple tasks for more e ective personalization.
Deep Cascade Multi-task Learning for Slot Filling in Online Shopping Assistant
Slot filling is a critical task in natural language understanding (NLU) for dialog systems.
Automatic Generation of Chinese Short Product Titles for Mobile Display
This paper studies the problem of automatically extracting a short title from a manually written longer description of E-commerce products for display on mobile devices.
Alternating Multi-bit Quantization for Recurrent Neural Networks
Recurrent neural networks have achieved excellent performance in many applications.
Cascade Ranking for Operational E-commerce Search
Real-world search applications often involve multiple factors of preferences or constraints with respect to user experience and computational costs such as search accuracy, search latency, size of search results and total CPU cost, while most existing search solutions only address one or two factors; 2).
